Dopamine: Differentially Private Federated Learning on Medical Data

Autor:	Malekzadeh, Mohammad, Hasircioglu, Burak, Mital, Nitish, Katarya, Kunal, Ozfatura, Mehmet Emre, Gündüz, Deniz
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
Popis:	While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine, a system to train DNNs on distributed datasets, which employs federated learning (FL) with differentially-private stochastic gradient descent (DPSGD), and, in combination with secure aggregation, can establish a better trade-off between differential privacy (DP) guarantee and DNN's accuracy than other approaches. Results on a diabetic retinopathy~(DR) task show that Dopamine provides a DP guarantee close to the centralized training counterpart, while achieving a better classification accuracy than FL with parallel DP where DPSGD is applied without coordination. Code is available at https://github.com/ipc-lab/private-ml-for-health. Comment: The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.11693 Zobrazit plný text záznamu View this record from Arxiv